تجزیه و تحلیل هزینه و منفعت توسعه انرژی پایدار با استفاده از ارزیابی منافع مشترک چرخه عمر و رویکرد پویایی سیستم
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23532||2014||10 صفحه PDF||سفارش دهید||6400 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Energy, Volume 119, 15 April 2014, Pages 57–66
A novel Air Resource Co-benefits model was developed to estimate the social benefits of a Sustainable Energy Policy, involving both renewable energy (RE) and energy efficiency improvements (EEI). The costs and benefits of the policy during 2010–2030 were quantified. A system dynamics model was constructed to simulate the amount of energy saving under the scenario of promoting both RE and EEI. The life-cycle co-reductions of five criteria pollutants (PM10, SO2, NOx, CO, and ozone) and greenhouse gas are estimated by assuming coal fired as marginal electricity suppliers. Moreover, a concise life table approach was developed to estimate averted years of life lost (YOLL). The results showed that YOLL totaling 0.11–0.21 years (41–78 days) per capita, or premature deaths totaling 126,507–251,169, is expected to be averted during 2010–2030 under the RE plus EEI scenario. Specifically, because of the higher investment cost, the benefit-cost ratio of 1.9–2.1 under the EEI scenario is lower than the 7.2–7.9 under the RE scenario. This difference reveals that RE is more socially beneficial than EEI. The net benefit of the RE and EEI scenarios during 2010–2030 totaled approximately US$ 5,972–6,893 per person or US$ 170–190 per MW h. To summarize, this study presents a new approach to estimate averted YOLL, and finds that the health benefits can justify the compliance costs associated with the Sustainable Energy Policy.
Increasing environmental burdens on both humans and ecosystems have highlighted issues of climate change and sustainable development and accelerated policy reform and innovation in renewable energy and energy conservation technology , , ,  and . Energy consumption in Taiwan increased by 135% from 1990 to 2010, and up to 99.3% of this energy is imported . The energy structure comprised electricity (49%), petroleum (40%), coal (8%), and natural gas (2.5%) in 2010. Taiwan’s total greenhouse gases (GHGs) emissions were 258.59 million metric tons of carbon dioxide equivalents (MtCO2e) in 2010, and per capita GHGs emissions were 11.58 tCO2e, ranking highest in Asia, and far above the world average of 4.38 tCO2e . Therefore, GHGs reduction is a pressing challenge for Taiwan. Research and development on renewable energy (RE) in the power generation and vehicular transportation industries, generally emphasizes GHGs reduction, and emphasizes energy efficiency improvements (EEI) in the residential, commercial, and industrial sectors. The Intergovernmental Panel on Climate Change (IPCC) reported that both RE and EEI have mitigation (namely GHGs emission abatement) and adaptation (namely reducing vulnerability to impacts of climate change) synergies with climate change . Co-benefit analysis integrates CO2 reduction with reduction of local criteria air pollutants. The criteria pollutants, include PM10, SO2, NOx, CO, and ozone, is listing in the Taiwan Air Pollution Control Act. All epidemiological studies of these pollutants have identified as harmful to human health , , ,  and . Avoided externalities, or external costs, such as environmental and health damages, achieved through the co-reduction of criteria air pollutants from CO2 reduction strategies, thus have been widely discussed. Nowadays, a variety of co-benefit analyses for CO2 reductions are performed when setting climate policy , , , , , , , ,  and . The European Commission launched the ExternE project in collaboration with the US Department of Energy since 1991 to assess the external cost of various different fuel cycles . ExternE was the first systematic study to use a bottom-up impact pathway approach (IPA), which helps quantify the environmental impacts and social costs of energy production and consumption. However, the study was limited to renewable energies such as wind, hydro, and biomass fuels. Various energy models, such as system dynamics (SD) and MARKAL (MARKet ALlocation) models, have been widely adopted to optimize energy deployment for CO2 emissions reduction scenarios by evaluating their corresponding economic impacts, namely gross domestic product (GDP) loss , , , ,  and . The SD approach is suitable for modeling dynamic environments, such as ecosystems and human activities, on a muti-dimensional scale with time-dependent variables . SD modeling has been applied for strategic energy planning and policy analysis since the early 1970s, starting with the well-known “Limits to Growth” and WORLD models. The SD software STELLA helps more clearly demonstrate the interactions of the environment and socio-economic variables, and also helps to identify the key factors that significantly alter a dynamic system  and . Some studies have incorporated internalization of externalities in energy system modeling , , ,  and . Pietrapertosa et al. (2009) integrated the ExternE, life cycle assessment (LCA), and MARKAL method to comprehensively analyze the external costs of energy systems . Chae and Park (2011) used the Environmental Benefits Mapping and Analysis Program (BenMAP) to perform the first local scale cost-benefit analysis and showed that Integrated Environmental Strategies (IES) outperform air quality management or GHG reduction measures alone . However, these studies regularly applied the IPA to estimate mortality benefits, which are the major portion of human health benefits, and are awaiting verification. This study thus aims to calculate both premature deaths avoided and the life table approach using the novel Air Resource Co-benefits (ARCoB) model. The main objective of this paper is to conduct a cost-benefit analysis to demonstrate the economic feasibility of the Sustainable Energy Policy Guidelines for climate change mitigation via the following steps: (a) to assess the life-cycle emissions of GHGs and air pollutants associated with electricity generation; (b) to predict changes in the unit costs of electricity generation technologies with the learning curve model; (c) to link the relationships among the renewable energy promotion, energy efficiency improvement, and energy pricing for modeling the evolution of electricity prices and electricity savings with the system dynamics approach; (d) to estimate the reductions of GHGs and air pollutants and evaluate the co-benefits from reduced exposure to air pollutants. The ARCoB model was implemented to evaluate the co-benefits of both RE and EEI improvement over the period 2010–2030. This study is the first cost-benefit analysis evaluating integrated strategies in the energy sector. Based on the positive findings, this methodology is recommended to energy sector authorities and policy-makers.
نتیجه گیری انگلیسی
This paper demonstrated the novel Air Resource Co-benefits model to evaluate the co-benefits of a sustainable energy policy, which involves both renewable energy promotion and energy efficiency improvement over the period 2010–2030. GHGs and air pollutant emissions were assumed to reduce proportionally with the substitution of renewable and installation of energy saving modules. Under the RE plus EEI scenario, the averted number of premature deaths is projected to be 126,507 during 2010–2030. The YOLL of 0.113 years (41 days) per capita is expected to be avoided. The mortality benefits estimated by VSL and VSLY vary little, which implies that both valuation measures are feasible. The investment cost is higher in the EEI scenario than the RE scenario, but the saved energy costs in the EEI scenario can exceed 50% of the investment cost of energy saving modules. Moreover, the unit health co-benefits of the RE are higher than those of the EEI. This difference reveals that it is more beneficial for society to promote RE than EEI. The results show that the external benefits, which include saved social costs such as reduced carbon emissions, averted morbidity, and averted mortality, significantly exceed the compliance or investment costs for the RE and EEI. Concerns regarding double counting may arise when health damage is related with ambient concentrations of the criteria pollutants (including PM10, SO2, NOx, CO, and ozone). However, the concentration–response functions used in this study were derived from multipollutant models, which consider correlation between air pollutants, minimize this concern. Moreover, this study does not recommend overlooking the health impact of pollutants other than PM10, based on SO2, NOx, and ozone are the major mortality risks in domestic epidemiological studies . The electricity savings simulated by the hypothetical system dynamics model are 24% higher than original forecast. This implies that the development of renewable energy can promote energy saving and to evaluate RE and EEI simultaneously is important to energy policy analysis. In conclusion, this study presents a new method for the economic valuation of air pollution mortality, primarily the life table approach, which is not addressed clearly in the ExternE reports or otherstudies. Furthermore, the results can justify the high compliance costs associated with the promotion of clean energy and energy saving, and can provide guidance for the creation of sustainable energy policy.